apache / airflow
Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Code health
apache/airflow scores 7.6 out of 10 on defect risk, which Repowise rates Good. Maintainability scores 8.0 out of 10. Static performance risk scores 9.9 out of 10. The three are scored separately and never blended into a single number. 394 of 10,389 files are git hotspots, the places where changes and bug fixes concentrate.
Full health report- Files
- 10k
- Symbols
- 82k
- Modules
- 400
- Doc pages
- 722
- Dead exports
- 999
Where the risk concentrates
All 394 hotspotsRanked by prior bug fixes and change frequency, mined from the full git history rather than from the code alone.
| File | Churn | Prior fixes | Maintainers | Commits 90d |
|---|---|---|---|---|
| test_scheduler_job.pyairflow-core/tests/unit/jobs | 100.0th %ile | 0 | 9 | 16 |
| test_utils.pyproviders/openlineage/tests/unit/openlineage/utils | 100.0th %ile | 0 | 14 | 4 |
| test_dagrun.pyairflow-core/tests/unit/models |
Read more about apache/airflow
- OverviewFiles, symbols, languages, packages, git intelligence
- ArchitectureDependency graph, layered architecture, symbol index, and third-party dependencies
- Code healthPer-file health scores, hotspots, test coverage, dead code, and refactoring targets
- AI explanationsProModule-by-module documentation generated from source
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Average health across every file, straight from the latest index.
[](https://repowise.dev/repo/apache/airflow)How apache/airflow works
Apache Airflow - A platform to programmatically author, schedule, and monitor workflows This page is an auto-generated, always-fresh map of the apache/airflow repository, written primarily in Python. Repowise indexes the source, parses every symbol, computes a dependency graph, scores per-file code health from complexity, duplication, test coverage and churn, mines git history for hotspots and ownership, and lifts the resulting architectural decisions into a wiki you can read or query through MCP.
The codebase has 10,389 source files, 81,627 symbols, and 17 languages, organised into 400 modules, led by python, typescript, external. Static analysis leaves 20,207 open findings across the three health dimensions.
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